Objective and quantitative evaluation of sleep quality is an important issue in medicine. It is known that wake and sleep can be distinguished using surface electroencephalogram (surf-EEG) recording. A deep sleep state can be visually recognized in surf-EEG recording by its slow wave activities (SWA). The deep sleep state is thus called slow wave sleep (SWS). SWS is an important sleep state for re-storage and recovery of our body and brain. During SWS, people are not easy to be woken up compared to other sleep states, and have stronger vagal tone, relatively low and stable cardiopulmonary activity.
Since 1960s, the evaluation of sleep states involves manual SWS scoring by doctors and special technicians. With a series of rules, sleep is divided into rapid eye movement (REM) sleep and non-REM (NREM) sleep. The latter is further divided into NREM 1, 2, 3 sleep, wherein NREM 3 is the so-called SWS. The visual scoring standards of SWS include (1) 0.5 Hz - 4 Hz SWA; (2) the amplitude of each slow wave more than 75 μv; (3) SWA occupying more than 20% of a 30-second epoch.
A major drawback of the conventional sleep evaluation techniques is that the visual scoring can often produce large differences among individual scorers. Moreover, conventional techniques rarely find SWS in the old age group despite that SWA-rebound has been demonstrated to exist in the old age group after sleep deprivation. The research results show that the rebound proportion and decay slope in the old age group nearly identical with those in the young age group. It is suggested that the inability to identify SWS in the old age group is caused by the arbitrary definitions of amplitude and percentage in the conventional sleep evaluation techniques.
Although attempts have been made to quantitatively evaluate sleep physiology using the intensity of SWA, an objective index for SWA quantification has yet to be established in clinic and research. Different laboratories presently choose different temporary methods to quantify sleep quality for individuals with Fast Fourier transform the most widely used analytical algorithm. These techniques are challenged by the large variety of SWA generation and synchronization in human brains, the differences in skull impedance between individuals, and noise and other signal contamination in the EEG signals.